Overview

Brought to you by YData

Dataset statistics

Number of variables9
Number of observations768
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory54.1 KiB
Average record size in memory72.2 B

Variable types

Numeric8
Categorical1

Alerts

Age is highly overall correlated with PregnanciesHigh correlation
Insulin is highly overall correlated with SkinThicknessHigh correlation
Pregnancies is highly overall correlated with AgeHigh correlation
SkinThickness is highly overall correlated with InsulinHigh correlation
Pregnancies has 111 (14.5%) zeros Zeros
BloodPressure has 38 (4.9%) zeros Zeros
SkinThickness has 227 (29.6%) zeros Zeros
Insulin has 374 (48.7%) zeros Zeros
BMI has 11 (1.4%) zeros Zeros
Age has 63 (8.2%) zeros Zeros

Reproduction

Analysis started2024-10-22 17:45:24.384547
Analysis finished2024-10-22 17:45:54.039939
Duration29.66 seconds
Software versionydata-profiling vv4.11.0
Download configurationconfig.json

Variables

Pregnancies
Real number (ℝ)

High correlation  Zeros 

Distinct15
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.28423997
Minimum0
Maximum1
Zeros111
Zeros (%)14.5%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2024-10-22T17:45:54.187262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.074074074
median0.22222222
Q30.44444444
95-th percentile0.74074074
Maximum1
Range1
Interquartile range (IQR)0.37037037

Descriptive statistics

Standard deviation0.2477153
Coefficient of variation (CV)0.8715006
Kurtosis-0.070852832
Mean0.28423997
Median Absolute Deviation (MAD)0.14814815
Skewness0.85396175
Sum218.2963
Variance0.061362871
MonotonicityNot monotonic
2024-10-22T17:45:54.557534image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0.07407407407 135
17.6%
0 111
14.5%
0.1481481481 103
13.4%
0.2222222222 75
9.8%
0.2962962963 68
8.9%
0.3703703704 57
7.4%
0.4444444444 50
 
6.5%
0.5185185185 45
 
5.9%
0.5925925926 38
 
4.9%
0.6666666667 28
 
3.6%
Other values (5) 58
7.6%
ValueCountFrequency (%)
0 111
14.5%
0.07407407407 135
17.6%
0.1481481481 103
13.4%
0.2222222222 75
9.8%
0.2962962963 68
8.9%
0.3703703704 57
7.4%
0.4444444444 50
 
6.5%
0.5185185185 45
 
5.9%
0.5925925926 38
 
4.9%
0.6666666667 28
 
3.6%
ValueCountFrequency (%)
1 4
 
0.5%
0.962962963 10
 
1.3%
0.8888888889 9
 
1.2%
0.8148148148 11
 
1.4%
0.7407407407 24
3.1%
0.6666666667 28
3.6%
0.5925925926 38
4.9%
0.5185185185 45
5.9%
0.4444444444 50
6.5%
0.3703703704 57
7.4%

Glucose
Real number (ℝ)

Distinct136
Distinct (%)17.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5189883
Minimum0
Maximum1
Zeros5
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2024-10-22T17:45:54.993250image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.25868726
Q10.38223938
median0.49343629
Q30.63706564
95-th percentile0.88880309
Maximum1
Range1
Interquartile range (IQR)0.25482625

Descriptive statistics

Standard deviation0.1926639
Coefficient of variation (CV)0.37122975
Kurtosis-0.13216039
Mean0.5189883
Median Absolute Deviation (MAD)0.12355212
Skewness0.41794622
Sum398.58301
Variance0.037119377
MonotonicityNot monotonic
2024-10-22T17:45:55.441591image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3822393822 17
 
2.2%
0.3884169884 17
 
2.2%
0.4563706564 14
 
1.8%
0.5675675676 14
 
1.8%
0.5428571429 14
 
1.8%
0.4254826255 14
 
1.8%
0.4625482625 13
 
1.7%
0.4378378378 13
 
1.7%
0.3575289575 13
 
1.7%
0.4193050193 13
 
1.7%
Other values (126) 626
81.5%
ValueCountFrequency (%)
0 5
0.7%
0.04247104247 1
 
0.1%
0.1166023166 1
 
0.1%
0.1227799228 2
 
0.3%
0.1474903475 1
 
0.1%
0.1536679537 1
 
0.1%
0.1722007722 1
 
0.1%
0.1845559846 1
 
0.1%
0.1907335907 3
0.4%
0.2092664093 4
0.5%
ValueCountFrequency (%)
1 1
 
0.1%
0.9938223938 1
 
0.1%
0.9876447876 4
0.5%
0.9814671815 3
0.4%
0.9752895753 2
0.3%
0.9691119691 3
0.4%
0.9629343629 2
0.3%
0.9505791506 1
 
0.1%
0.9444015444 1
 
0.1%
0.9382239382 4
0.5%

BloodPressure
Real number (ℝ)

Zeros 

Distinct42
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.49562355
Minimum0
Maximum1
Zeros38
Zeros (%)4.9%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2024-10-22T17:45:55.859775image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.051388889
Q10.375
median0.51388889
Q30.625
95-th percentile0.76388889
Maximum1
Range1
Interquartile range (IQR)0.25

Descriptive statistics

Standard deviation0.19718388
Coefficient of variation (CV)0.39785009
Kurtosis0.62049494
Mean0.49562355
Median Absolute Deviation (MAD)0.11111111
Skewness-0.40603553
Sum380.63889
Variance0.038881481
MonotonicityNot monotonic
2024-10-22T17:45:56.302788image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
0.4861111111 57
 
7.4%
0.5416666667 52
 
6.8%
0.4583333333 45
 
5.9%
0.5972222222 45
 
5.9%
0.5138888889 44
 
5.7%
0.4027777778 43
 
5.6%
0.625 40
 
5.2%
0.5694444444 39
 
5.1%
0 38
 
4.9%
0.3472222222 37
 
4.8%
Other values (32) 328
42.7%
ValueCountFrequency (%)
0 38
4.9%
0.04166666667 1
 
0.1%
0.06944444444 1
 
0.1%
0.125 4
 
0.5%
0.1527777778 2
 
0.3%
0.1805555556 5
 
0.7%
0.2083333333 13
 
1.7%
0.2361111111 11
 
1.4%
0.2638888889 11
 
1.4%
0.2777777778 2
 
0.3%
ValueCountFrequency (%)
1 7
0.9%
0.9861111111 3
 
0.4%
0.9583333333 2
 
0.3%
0.9305555556 1
 
0.1%
0.9027777778 3
 
0.4%
0.875 3
 
0.4%
0.8472222222 4
0.5%
0.8333333333 1
 
0.1%
0.8194444444 6
0.8%
0.7916666667 8
1.0%

SkinThickness
Real number (ℝ)

High correlation  Zeros 

Distinct51
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.25639648
Minimum0
Maximum1
Zeros227
Zeros (%)29.6%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2024-10-22T17:45:56.751559image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.2875
Q30.4
95-th percentile0.55
Maximum1
Range1
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.1980593
Coefficient of variation (CV)0.77247278
Kurtosis-0.98116285
Mean0.25639648
Median Absolute Deviation (MAD)0.15
Skewness0.026662981
Sum196.9125
Variance0.039227488
MonotonicityNot monotonic
2024-10-22T17:45:57.175828image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 227
29.6%
0.4 31
 
4.0%
0.375 27
 
3.5%
0.3375 23
 
3.0%
0.2875 22
 
2.9%
0.4125 20
 
2.6%
0.35 20
 
2.6%
0.225 20
 
2.6%
0.3875 19
 
2.5%
0.2375 18
 
2.3%
Other values (41) 341
44.4%
ValueCountFrequency (%)
0 227
29.6%
0.0875 2
 
0.3%
0.1 2
 
0.3%
0.125 5
 
0.7%
0.1375 6
 
0.8%
0.15 7
 
0.9%
0.1625 11
 
1.4%
0.175 6
 
0.8%
0.1875 14
 
1.8%
0.2 6
 
0.8%
ValueCountFrequency (%)
1 1
 
0.1%
0.7875 1
 
0.1%
0.75 1
 
0.1%
0.7 1
 
0.1%
0.675 2
0.3%
0.65 2
0.3%
0.6375 1
 
0.1%
0.625 3
0.4%
0.6125 3
0.4%
0.6 4
0.5%

Insulin
Real number (ℝ)

High correlation  Zeros 

Distinct157
Distinct (%)20.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.23152116
Minimum0
Maximum1
Zeros374
Zeros (%)48.7%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2024-10-22T17:45:57.731422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.095874263
Q30.4
95-th percentile0.92102161
Maximum1
Range1
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.29414862
Coefficient of variation (CV)1.2705042
Kurtosis0.40783879
Mean0.23152116
Median Absolute Deviation (MAD)0.095874263
Skewness1.1738981
Sum177.80825
Variance0.086523408
MonotonicityNot monotonic
2024-10-22T17:45:58.314441image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 374
48.7%
1 34
 
4.4%
0.3300589391 11
 
1.4%
0.4086444008 9
 
1.2%
0.4400785855 9
 
1.2%
0.3772102161 8
 
1.0%
0.295481336 7
 
0.9%
0.3143418468 7
 
0.9%
0.5658153242 7
 
0.9%
0.3614931238 6
 
0.8%
Other values (147) 296
38.5%
ValueCountFrequency (%)
0 374
48.7%
0.04400785855 1
 
0.1%
0.04715127701 1
 
0.1%
0.05029469548 1
 
0.1%
0.05658153242 2
 
0.3%
0.06915520629 1
 
0.1%
0.07229862475 2
 
0.3%
0.07858546169 1
 
0.1%
0.09115913556 1
 
0.1%
0.100589391 1
 
0.1%
ValueCountFrequency (%)
1 34
4.4%
0.9996070727 1
 
0.1%
0.974459725 1
 
0.1%
0.9555992141 1
 
0.1%
0.9430255403 1
 
0.1%
0.921021611 2
 
0.3%
0.9147347741 1
 
0.1%
0.8958742633 2
 
0.3%
0.8927308448 1
 
0.1%
0.8801571709 1
 
0.1%

BMI
Real number (ℝ)

Zeros 

Distinct242
Distinct (%)31.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.50470605
Minimum0
Maximum1
Zeros11
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2024-10-22T17:45:58.870533image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.22715054
Q10.375
median0.50134409
Q30.625
95-th percentile0.83454301
Maximum1
Range1
Interquartile range (IQR)0.25

Descriptive statistics

Standard deviation0.18950494
Coefficient of variation (CV)0.37547586
Kurtosis0.050052852
Mean0.50470605
Median Absolute Deviation (MAD)0.12365591
Skewness0.1358086
Sum387.61425
Variance0.035912121
MonotonicityNot monotonic
2024-10-22T17:45:59.327063image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.501344086 13
 
1.7%
0.4905913978 12
 
1.6%
0.4798387097 12
 
1.6%
0 11
 
1.4%
0.5362903226 10
 
1.3%
0.5120967742 10
 
1.3%
0.4502688172 9
 
1.2%
0.5228494624 9
 
1.2%
0.5255376344 9
 
1.2%
0.4690860215 9
 
1.2%
Other values (232) 664
86.5%
ValueCountFrequency (%)
0 11
1.4%
0.1303763441 3
 
0.4%
0.1357526882 1
 
0.1%
0.1545698925 1
 
0.1%
0.1599462366 1
 
0.1%
0.1626344086 1
 
0.1%
0.1653225806 2
 
0.3%
0.1680107527 3
 
0.4%
0.1760752688 1
 
0.1%
0.1787634409 1
 
0.1%
ValueCountFrequency (%)
1 8
1.0%
0.9852150538 1
 
0.1%
0.9771505376 1
 
0.1%
0.9744623656 1
 
0.1%
0.9663978495 1
 
0.1%
0.9529569892 1
 
0.1%
0.939516129 1
 
0.1%
0.9287634409 2
 
0.3%
0.8991935484 2
 
0.3%
0.8965053763 1
 
0.1%

DiabetesPedigreeFunction
Real number (ℝ)

Distinct517
Distinct (%)67.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.16817946
Minimum0
Maximum1
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2024-10-22T17:45:59.847785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.026622545
Q10.070772844
median0.12574722
Q30.23409479
95-th percentile0.45040564
Maximum1
Range1
Interquartile range (IQR)0.16332195

Descriptive statistics

Standard deviation0.1414725
Coefficient of variation (CV)0.84119962
Kurtosis5.5949535
Mean0.16817946
Median Absolute Deviation (MAD)0.071520068
Skewness1.9199111
Sum129.16183
Variance0.020014468
MonotonicityNot monotonic
2024-10-22T17:46:00.355893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.07685738685 6
 
0.8%
0.07514944492 6
 
0.8%
0.08112724167 5
 
0.7%
0.05508112724 5
 
0.7%
0.0781383433 5
 
0.7%
0.07728437233 5
 
0.7%
0.0683176772 5
 
0.7%
0.04782237404 4
 
0.5%
0.07899231426 4
 
0.5%
0.09436379163 4
 
0.5%
Other values (507) 719
93.6%
ValueCountFrequency (%)
0 1
0.1%
0.002561912895 1
0.1%
0.002988898377 2
0.3%
0.004269854825 2
0.3%
0.004696840307 1
0.1%
0.005977796755 1
0.1%
0.007685738685 1
0.1%
0.009393680615 1
0.1%
0.009820666097 1
0.1%
0.01024765158 1
0.1%
ValueCountFrequency (%)
1 1
0.1%
0.9611443211 1
0.1%
0.9436379163 1
0.1%
0.8791631085 1
0.1%
0.7749786507 1
0.1%
0.7271562767 1
0.1%
0.7058070026 1
0.1%
0.6921434671 1
0.1%
0.6917164816 1
0.1%
0.6498719044 1
0.1%

Age
Real number (ℝ)

High correlation  Zeros 

Distinct47
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.26812901
Minimum0
Maximum1
Zeros63
Zeros (%)8.2%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2024-10-22T17:46:00.871909image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.065934066
median0.17582418
Q30.43956044
95-th percentile0.81318681
Maximum1
Range1
Interquartile range (IQR)0.37362637

Descriptive statistics

Standard deviation0.25556932
Coefficient of variation (CV)0.95315804
Kurtosis0.33096993
Mean0.26812901
Median Absolute Deviation (MAD)0.15384615
Skewness1.0671703
Sum205.92308
Variance0.065315676
MonotonicityNot monotonic
2024-10-22T17:46:01.882342image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0.02197802198 72
 
9.4%
0 63
 
8.2%
0.08791208791 48
 
6.2%
0.06593406593 46
 
6.0%
0.04395604396 38
 
4.9%
0.1538461538 35
 
4.6%
0.1098901099 33
 
4.3%
0.1318681319 32
 
4.2%
0.1758241758 29
 
3.8%
0.2197802198 24
 
3.1%
Other values (37) 348
45.3%
ValueCountFrequency (%)
0 63
8.2%
0.02197802198 72
9.4%
0.04395604396 38
4.9%
0.06593406593 46
6.0%
0.08791208791 48
6.2%
0.1098901099 33
4.3%
0.1318681319 32
4.2%
0.1538461538 35
4.6%
0.1758241758 29
3.8%
0.1978021978 21
 
2.7%
ValueCountFrequency (%)
1 9
1.2%
0.989010989 4
0.5%
0.967032967 3
 
0.4%
0.9450549451 1
 
0.1%
0.9230769231 4
0.5%
0.9010989011 4
0.5%
0.8791208791 2
 
0.3%
0.8571428571 5
0.7%
0.8351648352 3
 
0.4%
0.8131868132 7
0.9%

Outcome
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
0.0
500 
1.0
268 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2304
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 500
65.1%
1.0 268
34.9%

Length

2024-10-22T17:46:02.171838image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-22T17:46:02.421295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 500
65.1%
1.0 268
34.9%

Most occurring characters

ValueCountFrequency (%)
0 1268
55.0%
. 768
33.3%
1 268
 
11.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2304
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1268
55.0%
. 768
33.3%
1 268
 
11.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2304
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1268
55.0%
. 768
33.3%
1 268
 
11.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2304
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1268
55.0%
. 768
33.3%
1 268
 
11.6%

Interactions

2024-10-22T17:45:51.361378image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-22T17:45:25.320399image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-22T17:45:29.808484image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-22T17:45:36.600304image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-22T17:45:40.557967image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-22T17:45:43.678670image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-22T17:45:46.558874image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-22T17:45:49.225415image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-22T17:45:51.637157image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-22T17:45:25.916869image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-22T17:45:30.775795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-22T17:45:37.188726image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-22T17:45:41.069638image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-22T17:45:44.081909image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-22T17:45:46.959659image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-22T17:45:49.488562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-22T17:45:51.885124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-22T17:45:26.505637image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-22T17:45:31.549595image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-22T17:45:37.519073image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-22T17:45:41.507520image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-22T17:45:44.472790image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-22T17:45:47.210219image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-22T17:45:49.734373image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-22T17:45:52.119990image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-22T17:45:26.960391image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-22T17:45:32.379734image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-22T17:45:38.023355image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-22T17:45:41.773911image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-22T17:45:44.804311image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-22T17:45:47.475384image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-22T17:45:50.014840image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-22T17:45:52.353564image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-22T17:45:27.403963image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-22T17:45:34.317144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-22T17:45:38.374835image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-22T17:45:42.039548image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-22T17:45:45.146957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-22T17:45:47.727292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-22T17:45:50.256196image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-22T17:45:52.620144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-22T17:45:27.902987image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-22T17:45:34.899631image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-22T17:45:38.847121image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-22T17:45:42.378370image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-22T17:45:45.490936image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-22T17:45:47.966619image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-22T17:45:50.548489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-22T17:45:52.871031image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-22T17:45:28.320064image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-22T17:45:35.615925image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-22T17:45:39.363150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-22T17:45:43.018979image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-22T17:45:45.868187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-22T17:45:48.220124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-22T17:45:50.829022image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-22T17:45:53.109633image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-22T17:45:28.807689image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-22T17:45:36.165980image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-22T17:45:40.052077image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-22T17:45:43.312825image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-22T17:45:46.204817image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-22T17:45:48.494713image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-22T17:45:51.090351image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-10-22T17:46:02.604061image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
AgeBMIBloodPressureDiabetesPedigreeFunctionGlucoseInsulinOutcomePregnanciesSkinThickness
Age1.0000.1310.3510.0430.285-0.1140.3290.607-0.067
BMI0.1311.0000.2920.1410.2310.1920.3170.0000.444
BloodPressure0.3510.2921.0000.0300.236-0.0070.1500.1850.126
DiabetesPedigreeFunction0.0430.1410.0301.0000.0910.2210.173-0.0430.180
Glucose0.2850.2310.2360.0911.0000.2130.4840.1310.060
Insulin-0.1140.192-0.0070.2210.2131.0000.265-0.1260.541
Outcome0.3290.3170.1500.1730.4840.2651.0000.2480.207
Pregnancies0.6070.0000.185-0.0430.131-0.1260.2481.000-0.085
SkinThickness-0.0670.4440.1260.1800.0600.5410.207-0.0851.000

Missing values

2024-10-22T17:45:53.455190image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-22T17:45:53.873494image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

PregnanciesGlucoseBloodPressureSkinThicknessInsulinBMIDiabetesPedigreeFunctionAgeOutcome
00.4444440.6849420.5138890.43750.0000000.5443550.2344150.6373631.0
10.0740740.2957530.4305560.36250.0000000.3561830.1165670.2197800.0
20.5925930.9011580.4027780.00000.0000000.2674730.2536290.2417581.0
30.0740740.3204630.4305560.28750.2954810.3965050.0380020.0000000.0
40.0000000.6169880.0694440.43750.5280940.7997310.9436380.2637361.0
50.3703700.4872590.5416670.00000.0000000.3293010.0525190.1978020.0
60.2222220.2525100.2083330.40000.2766210.4744620.0725880.1098901.0
70.7407410.4810810.0000000.00000.0000000.5900540.0239110.1758240.0
80.1481480.9876450.4861110.56251.0000000.4610220.0341590.7032971.0
90.5925930.5428570.8472220.00000.0000000.0000000.0657560.7252751.0
PregnanciesGlucoseBloodPressureSkinThicknessInsulinBMIDiabetesPedigreeFunctionAgeOutcome
7580.0740740.4254830.5694440.00000.0000000.6491940.0508110.1098900.0
7590.4444440.9444020.7916670.00000.0000000.5954300.0853970.9890111.0
7600.1481480.3142860.3194440.32500.0502950.4045700.2937660.0219780.0
7610.6666670.8208490.5416670.38750.0000000.8239250.1387700.4835161.0
7620.6666670.3204630.3750000.00000.0000000.2459680.0273270.2637360.0
7630.7407410.3945950.5694440.60000.5658150.5255380.0397100.9230770.0
7640.1481480.5243240.4861110.33750.0000000.6303760.1118700.1318680.0
7650.3703700.5181470.5138890.28750.3520630.3454300.0713070.1978020.0
7660.0740740.5490350.3472220.00000.0000000.4502690.1157130.5714291.0
7670.0740740.3451740.4861110.38750.0000000.4583330.1011960.0439560.0